Dynamic Task Scheduling in Cloud Robotics for Healthcare and Manufacturing using Fuzzy Logic and Metaheuristics
Keywords:
Cloud robotics, task scheduling, fuzzy logic, metaheuristics, healthcare, manufacturing, optimization, resource allocation, execution time, efficiency.Abstract
Background: Cloud robotics is an integration of cloud computing with robotics, which will bring
efficiency to task management in both healthcare and manufacturing sectors. Optimization of
performance needs effective scheduling and resource allocation.
Objectives: This research is aimed at the optimization of dynamic task scheduling with fuzzy logic
and metaheuristics in healthcare and manufacturing settings for enhanced task allocation and
execution.s
Methods: We adopted a hybrid methodology to integrate decision-making using fuzzy logic with
optimization techniques of metaheuristic algorithms in order to optimally schedule the tasks.
Parameters include resource usage, task priorities, and the time taken by each task during
execution.
Results: The result showed that it is possible to have 92% accuracy on the allocation of tasks with
the proposed model where 35% execution time improvement compared to conventional
approaches.Conclusion: The use of fuzzy logic and metaheuristics greatly enhances the task scheduling in
cloud robotics, thereby improving the efficiency and the resource utilization. Scalability and real
time applications are future work.
